@inproceedings{ito2013context,
author = {Ito, Jonathan and Marsella, Stacy},
title = {Context dependent utility: modeling decision behavior across contexts},
booktitle = {Proceedings of 35th Annual Conference of the Cognitive Science Society
(to appear)},
year = {2013},
abstract = {One significant challenge in creating accurate models of human decision
behavior is accounting for the effect of context. Research shows
that seemingly minor changes in the presentation of a decision can
lead to drastic shifts in behavior; phenomena collectively referred
to as framing effects. Previous work has developed Context Dependent
Utility (CDU), a framework integrating Appraisal Theory with decision-theoretic
principles. This work extends existing research by presenting a study
exploring the behavioral predictions offered by CDU regarding the
multidimensional effect of context on decision behavior.
The present study finds support for the predictions of CDU regarding
the impact of context on decisions: 1) as perceptions of pleasantness
increase, decision behavior tends towards risk-aversion; 2) as perceptions
of goal-congruence increase, decision behavior tends towards risk-aversion;
3) as perceptions of controllability increase, i.e., perceptions
that outcomes would have been primarily caused by the decision maker,
behavior tends towards risk-seeking.},
file = {ito2013context.pdf:ito2013context.pdf:PDF},
url = {ito2013context.pdf},
owner = {jito},
quality = {1},
timestamp = {2013.04.14}
}
@inproceedings{ito2011contextually,
author = {Ito, J. and Marsella, S.},
title = {Contextually-based utility: An appraisal-based approach at modeling
framing and decisions},
booktitle = {Proceedings of the Twenty-Fifth $\{$AAAI$\}$ Conference on Artificial Intelligence},
year = {2011},
volume = {2},
pages = {60--65},
file = {ito2011contextually.pdf:ito2011contextually.pdf:PDF},
abstract = {
Creating accurate computational models of human decision making is a vital step towards the realization of socially intelligent systems capable of both predicting and simulating human behavior. In modeling human decision making, a key factor is the psychological phenomenon known as "framing", in which the preferences of a decision maker change in response to contextual changes in decision problems. Existing approaches treat framing as a one-dimensional contextual influence based on the perception of outcomes as either gains or losses. However, empirical studies have shown that framing effects are much more multifaceted than one-dimensional views of framing suggest. To address this limitation, we propose an integrative approach to modeling framing which combines the psychological principles of cognitive appraisal theories and decision-theoretic notions of utility and probability. We show that this approach allows for both the identification and computation of the salient contextual factors in a decision as well as modeling how they ultimately affect the decision process. Furthermore, we show that our multi-dimensional, appraisal-based approach can account for framing effects identified in the empirical literature which cannot be addressed by one-dimensional theories, thereby promising more accurate models of human behavior.},
url = {ito2011contextually.pdf}
}
@article{ito2010modeling,
author = {Ito, Jonathan and Pynadath, David and Marsella, Stacy},
title = {Modeling self-deception within a decision-theoretic framework},
journal = {Autonomous Agents and Multi-Agent Systems},
year = {2010},
volume = {20},
pages = {3--13},
number = {1},
month = jan,
abstract = {Computational modeling of human belief maintenance and decision-making
processes has become increasingly important for a wide range of applications.
In this paper, we present a framework for modeling the human capacity
for self-deception from a decision-theoretic perspective in which
we describe an integrated process of wishful thinking which includes
the determination of a desired belief state, the biasing of internal
beliefs towards or away from this desired belief state, and the final
decision-making process. Finally, we show that in certain situations
self-deception can be beneficial.},
day = {01},
doi = {10.1007/s10458-009-9096-7},
file = {ito2010modeling.pdf:ito2010modeling.pdf:PDF},
timestamp = {2010.03.30},
url = {ito2010modeling.pdf}
}
@inproceedings{ito2010wishful,
author = {Jonathan Y. Ito and David V. Pynadath and Liz Sonenberg and Stacy
C. Marsella},
title = {Wishful Thinking in Effective Decision Making (Extended Abstract)},
booktitle = {Proceedings of the 9th International Conference on Autonomous Agents
and Multiagent Systems (AAMAS 2010).},
year = {2010},
abstract = {Creating agents that act reasonably in uncertain environments is a
primary goal of agent-based research. In this work we explore the
theory that wishful thinking can be an effective strategy in uncertain
and competitive decision scenarios. Specifically, we present the
constraints necessary for wishful thinking to outperform Expected
Utility Maximization and take instances of popular games from Game-Theoretic
literature showing how they relate to our constraints and whether
they can benefit from wishful-thinking.},
file = {ito2010wishful.pdf:ito2010wishful.pdf:PDF},
journal = {Autonomous Agents and Multi-Agent Systems},
owner = {jito},
url = {ito2010wishful.pdf}
}
@inproceedings{ItoPM09selfdeceptive,
author = {Jonathan Y. Ito and David V. Pynadath and Stacy C. Marsella},
title = {Self-Deceptive Decision Making: Normative and Descriptive Insights},
booktitle = {Proceedings of the Conference on Autonomous Agents and Multiagent
Systems {AAMAS}},
year = {2009},
editor = {Carles Sierra and Cristiano Castelfranchi and Keith S. Decker and
Jaime Sim{\~a}o Sichman},
volume = {2},
pages = {1113-1120},
month = {May},
publisher = {IFAAMAS},
abstract = {Computational modeling of human belief maintenance and decision-making
processes has become increasingly important for a wide range of applications.
We present a framework for modeling the psychological phenomenon
of self-deception in a decision-theoretic framework. Specifically,
we model the self-deceptive behavior of wishful thinking as a psychological
bias towards the belief in a particularly desirable situation or
state. By leveraging the structures and axioms of expected utility
(EU) we are able to operationalize both the determination and the
application of the desired belief state with respect to the decision-making
process of expected utility maximization. While we categorize our
framework as a descriptive model of human decision making, we show
that when specific errors are present, the realized expected utility
of an action biased by wishful thinking can exceed that of an action
motivated purely by the maximization of expected utility. Finally,
in order to provide a descriptive characterization of our framework,
we present a discussion of wishful thinking with respect to the Certainty
Effect and the Allais Paradox, two specific documented inconsistencies
of human behavior. In this discussion we show that our framework
has the descriptive flexibility needed to account for both the Certainty
Effect and Allais Paradoxes.},
category = {Emotion Modeling},
url = {ItoPM09selfdeceptive.pdf}
}
@inproceedings{ItoPM08modeling,
author = {Jonathan Y. Ito and David V. Pynadath and Stacy C. Marsella},
title = {Modeling Self-deception within a Decision-Theoretic Framework},
booktitle = {Proceedings of the Conference of Intelligent Virtual Agents {IVA}},
year = {2008},
editor = {Helmut Prendinger and James C. Lester and Mitsuru Ishizuka},
volume = {5208},
series = {Lecture Notes in Computer Science},
pages = {322-333},
month = {September},
publisher = {Springer},
abstract = {Computational modeling of human belief maintenance and decision-making
processes has become increasingly important for a wide range of applications.
In this paper, we present a framework for modeling the human capacity
for self-deception from a decision-theoretic perspective in which
we describe processes for determining a desired belief state, the
biasing of internal beliefs towards the desired belief state, and
the actual decision-making process based upon the integrated biases.
Furthermore, we show that in some situations self-deception can be
beneficial.},
bibsource = {DBLP, http://dblp.uni-trier.de},
category = {Emotion Modeling},
ee = {http://dx.doi.org/10.1007/978-3-540-85483-8_33},
isbn = {978-3-540-85482-1},
url = {ItoPM08modeling.pdf}
}
@inproceedings{ItoPM07decision,
author = {Jonathan Y. Ito and David V. Pynadath and Stacy C. Marsella},
title = {A Decision-Theoretic Approach to Evaluating Posterior Probabilities
of Mental Models},
booktitle = {Proceedings of the {AAAI} Workshop on Plan, Activity, and Intent
Recognition ({PAIR}-07) },
year = {2007},
editor = {Christopher Geib and David Pynadath},
volume = {WS-07-09},
series = {AAAI Technical Report},
pages = {60-65},
month = {July},
publisher = {AAAI Press},
abstract = {Agents face the problem of maintaining and updating their beliefs
over the possible mental models (whether goals, plans, activities,
intentions, etc.) of other agents in many multiagent domains. Decision-theoretic
agents typically model their uncertainty in these beliefs as a probability
distribution over their possible mental models of others. They then
update their beliefs by computing a posterior probability over mental
models conditioned on their observations. We present a novel algorithm
for performing this belief update over mental models that are in
the form of Partially Observable Markov Decision Problems (POMDPs).
POMDPs form a common model for decision-theoretic agents, but there
is no existing method for translating a POMDP, which generates deterministic
behavior, into a probability distribution over actions that is appropriate
for abductive reasoning. In this work, we explore alternate methods
to generate a more suitable probability distribution. We use a sample
multiagent scenario to demonstrate the different behaviors of the
approaches and to draw some conclusions about the conditions under
which each is successful.},
category = {Emotion Modeling},
url = {ItoPM07decision.pdf}
}
@inproceedings{donnelly2003effects,
author = {Donnelly, J. and Edwards, G. and Haglich, P. and Ito, J. and Olin,
K. and Padgett, T.},
title = {Effects-based planning with strategy templates and semantic support},
booktitle = {AeroSense 2003},
year = {2003},
pages = {27--35},
organization = {International Society for Optics and Photonics},
abstract = {To implement effects-based operations, Joint Air Operations planners
must think in terms of achieving desired effects in the strategic
campaign through operational course of action levels of planning.
The strategy development tools discussed in this paper were designed
specifically to encourage effects-based thinking. The tools are used
to build plans, plan fragments and, most importantly, "strategy templates".
Strategy templates are knowledge-level skeletal planning models that
guide the design of strategies that specify the necessary mechanisms
and actions to achieve desired effects in the battlespace. The strategic
planning knowledge captured in the templates may be employed through
wizards to help human planners rapidly apply these general strategic
models to specific planning problems. To support the abstract concepts
required in the templates, and to guide plan authors in applying
these abstract templates to real battlespace planning problems and
data, we employ a semantic engine to support the tool capabilities.
This engine exploits ontologies represented in the DARPA Agent Markup
Language (DAML) and employs the Java Expert System Shell (Jess) as
the inference engine to implement the axioms and theorems that encapsulate
the DAML semantics. This paper will discuss this technology in supporting
Effects-based Operations and its application into Command and Control
for Joint Air Operations for kinetic and non-kinetic military operations.}
}
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